4 - abernethy & brownell 1997 - management control systems

8/19/2019 4 - Abernethy & Brownell 1997 - Management Control Systems

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Accounti ng, Or ganizations and Society,

Vol.

22, NO.

3/4, pp. 233-248, 1997

c 1997 Elsevier Science Ltd

All rights resewed. Printed in Great Britain

0361.3682/97 17.00+0.00

PII: SO361-3682(96)00038-4

MANAGEMENT CONTROL SYSTEMS IN RESEARCH AND DEVELOPMENT

ORGANIZATIONS: THE ROLE OF ACCOUNTING, BEHAVIOR AND

PERSONNEL CONTROLS

MARGARET A. ABERNETHY and PETER BROWNELL

i e Un i v er si t y o f Me l bou r ne

bstract

This paper reports the result of an empirical study examining the role of accounting and non-accounting

controls in a research and development setting. The paper draws on Pet-row’s model of technology and

structure to explore the influence of task characteristics on the effectiveness of accounting, behavior and

personnel forms of control (Perrow, 1970). The main contribution of the study is the finding that non-

accounting controls, especially personnel forms of control, contribute to organization effectiveness, par-

ticularly where task characteristics are not well suited to the use of accounting-based controls. In addition,

the results suggest that of the two task dimensions examined, task analyzability and number of excep

tions, the latter proves to have the more significant inIIuence on the suitability of the controls.

“Programmed” types of controls (such as either accounting or behavior controls) appear unsuitable

where number of exceptions is high. 8 1997 Elsevier Science Ltd

Much management accounting research has

focused on the nature of control system design

in manufacturing production settings where

tasks are considered to be particularly well sui-

ted to the use of accounting-based controls.

Considerable attention is now being directed

towards understanding the role of accounting

controls in other settings where the character-

istics of the tasks undertaken bring into ques-

tion the suitability of conventional, accountmg-

based controls and raise the prospect that for

effective control, organizations will need to

design their control systems around a variety of

non-accounting controls (Abernethy 8~ Stoel-

winder, 1995; Rockness & Shields, 1988; Foster

& Gupta, 1994). This study contributes to this

body of literature by examining management

control systems (MC ) in research and devel-

opment (R&D) organizations.

The distinctive feature of R&D tasks which

potentially influences the effectiveness of

accounting controls is, at least in some cases,

their lack of routineness. Perrow’s model of

technology and structure highlighted two key

dimensions of routineness: the existence (or

otherwise) of well-established techniques for

performing tasks (task analyzability), and the

degree of variety in the tasks encountered

(number of exceptions) (Pet-row, 1970). The

two dimensions are commonly, and collectively,

The research reported in this paper was supported by an Australian Research Council (Large) Grant to the second-named

author. The authors appreciate greatly the constructive comments received on this, or earlier drafts, from Rob Chenhall,

Ken Merchant, Nick Dopuch, Kevin Davis, Alan Dunk, participants in workshops at The University of Melbourne, The

University of Sydney, The University of Southern California, The University of Queensland, The University of Auckland, the

1992 AAANZ Conference (Palmerston North, New Zealand), the 1992 Management Accounting Research Conference

(University of New South Wales), the 1994 Annual Meeting of the American Accounting Association (New York), and two

anonymous reviewers.

233


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M. A. ABERNETHY and P. BROWNELL

referred to as task uncertainty and the results of

stood, nor extensively researched, and because

research studies in management accounting

of the desirability of triangulating the assess-

have persistently shown that accounting forms ment of the non-accounting controls studied,

of control are poorly suited to highly uncertain

an interview phase supplemented the ques-

tasks (Hirst, 1983; Brownell & Hirst, 1986;

tionnaire which was used to gather the primary

Brownell & Dunk, 1991). data.

Despite the apparent appeal of these argu-

ments, evidence obtained from empirical stu-

dies of R&D settings is sparse, and that which

does exist is mixed. For example, Rockness and

Shields (1984) while able to show that the

importance of differing types of controls was

associated with knowledge of the task transfor-

mation process, were unable to find evidence

that measurability of outputs, task complexity

and task dependence mattered.

The balance of the paper is structured as fol-

lows. In the next section, the theoretical fra-

mework underlying the study is developed.

This leads to the statement of hypotheses. Fol-

lowing that, the empirical study is described,

with sections addressing the method, sample

and results. The paper concludes with a recog-

nition of some of the limitations of the study,

and some avenues for future research.

This study seeks to extend the research of

Rockness and Shields (1984,1988) in several

ways. First, the study introduces a criterion

variable (managerial performance) which facili-

tates a more complete test of the theoretical

propositions of interest. Rockness and Shields

restricted their tests to an assessment of the

covariation of task characteristics and control

system elements and did not attempt to evalu-

ate whether the “right” calibration of control

system design and task characteristics was

associated with important organizational out-

comes. Second, the study addresses the mea-

surement issues associated with the manner in

which task characteristics were operationalized

by Rockness and Shields (1984,1988). The two

task dimensions examined in this study are

empirically assessed using an established

instrument (Withey

et a l .

1983). Rockness and

Shields conducted their study before the publi-

cation of the Withey

et a l .

(1983) study, and

acknowledged the limitations associated with

their measurement of the task dimensions.

Third, we expand the array of controls available

for coordinating and controlling tasks by

examining the use of accounting, behavior and

personnel controls. It is hypothesized that dif-

fering combinations of the two task variables

give rise to differing relations between reliance

on each of the three types of controls, and R&D

management performance. Fourth, because the

R&D setting is neither particularly well under-

THEORETICAL. FRAMEWORK

Perrow (1970) argued that organizational struc-

ture and, in particular, reliance on a bureau-

cratic organizational form, will be dependent

upon the degree of “routineness” of task

technology. “Routineness” is unbundled by

Perrow into the two dimensions, described

above, of task analyzability and number of

exceptions. Where established techniques for

handling tasks do not exist (low analyzability),

or where there exists substantial variety or

novelty in the tasks encountered (high number

of exceptions), Perrow describes the task set-

ting as “non-routine”, and conversely when

tasks are analyzable with few exceptions, the

task setting is “routine”. Perrow proceeds to

describe the structural arrangements necessary

to achieve effective control and co-ordination

for the different task environments faced by an

organization. Figure 1 presents a diagrammatic

representation of Perrow’s framework.

Tasks in Cell 1 of Fig. 1 are repetitive and

familiar (few exceptions), with known routines

for handling them (high analyzability). Long

production runs of highly standardized pro-

ducts would be typical here, and this setting is

readily recognized as one where conventional

accounting controls, such as standard costing

and flexible budgeting, are well suited (Bruns &

Waterhouse, 1975). With little task variety and


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MANAGEMENT CONTROL SYSTEMS IN R&D ORGANIZATIONS

235

a clear view of input-output relations in task

In contrast, Cell 3 represents the “non-

execution, the organization can justify the

routine” situation and it is here that Perrow

development and use of these forms of control.

expects that formal, bureaucratic controls will

Perrow also argued that organizations facing not be effective for controlling performance.

such tasks would be able to rely on procedure

Tasks cannot be “programmed” and thus beha-

guides, operating manuals, job codification and vior cannot be controlled by implementing

rigid lines of reporting and accountability for procedures which pre-specify desired actions

controlling employee behavior. In this paper, or by monitoring individual actions through the

we refer to these collectively as “behavior” use of supervisors. Perrow argues that “profes-

controls, the effectiveness of which requires sional or collegial” structural arrangements are

the existence of tasks which are transparent in required in this setting. The training of profes-

terms of input-output knowledge, and pre- sionals and their socialization is based on a col-

dictable or regular in terms of task variety. In legial model of control which regulates

this setting, the choice between accounting behavior primarily through self and peer group

controls, and behavior controls, will be dictated control processes. Organizations operationalize

by their relative cost and issues of timeliness. these self and group controls through the use of

Generally, where timeliness is at a premium, selection and training policies/procedures

and behavior controls are feasible, they will be

which ensure that people who have been

preferred to accounting controls because they

exposed to appropriate training and socializa-

operate in “real-time” during task execution, tion processes are employed. We follow Mer-

rather than “ex post”, as with accounting con- chant (1985) and refer to this as “personnel”

trols (Merchant, 1985). control.

Low

Number of

Exceptions

High

High

Task Analyzability

Low

Formal

Administrative

Controls

1

2

4 3

Informal Co-ordination

Mechanisms

Fig. 1. Perrow’s Model of Structure and Technology (Perrow, 1970)


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236

M. A. ABERNE’IHY and P. BROWNELI

The above arguments can be summarized in

terms of the first two hypotheses of the study:

Hl: Where tasks are highly analyzable

and exceptions are few, reliance on both

accounting and behavior controls will be

positively related to R&D management

performance.

H2: Where task analyzability is low and

exceptions are many and frequent, reli-

ance on personnel controls will be posi-

tively related to R&D management

performance.

The remaining two situations depicted in

Fig. 1 (Cells 2 and 4) involve task settings

where it is more difficult to predict which

forms of control will prove most effective.

Tasks may have little variety, yet exhibit uncer-

tainty in the transformation process (Cell 2);

others may have little uncertainty but a great

deal of variety (Cell 4). They are in the middle

of the routine/non-routine continuum, but they

are not the same; they differ from one another

(Perrow, 1970). We attempt to extend prior lit-

erature by developing and testing hypothesized

relations between control system choices, and

managerial performance, in Cells 2 and 4 of

Fig. 1.

Considering Cell 2 first, tasks here are low in

variety suggesting the potential suitability of

behavior controls. However, notwithstanding

the lack of variety of tasks, little has been

learned about the process by which inputs are

transformed into outputs; programs for per-

forming these tasks have not been established

(i.e., analyzability is low). It is, therefore, not

possible to develop a set of manuals, guides or

procedures which can be drawn upon during

task performance. Thus, behavior controls will

be unsuitable in this task setting. However,

accounting controls may offer some potential

here. Budgets are a planning device used to set

performance targets and subsequently serve to

measure the results of individual actions (Mer-

chant, 1985). Use of accounting controls in this

manner is not dependent upon identifying

input-output relations but simply requires the

ability to pre-determine and quantify desired

outputs. The means of achieving these targets

are not specified (unlike standard costing sys-

tems) but rather left to the judgment and

experience of those performing the tasks. One

can think of the sales function as being con-

trolled in this manner. Little is known regarding

how the range of marketing inputs (advertising,

pricing, promotions and so forth) combines

with sales effort to translate into task perfor-

mance (Rockness & Shields, 19SS), yet the

variety of problems faced is relatively limited.

Budget targets which incorporate expense

allowances as a percentage of the sales dollar (a

form of flexible budgeting) could be an effec-

tive form of control in Cell 2.

In Cell 4, much variety is encountered in

tasks, but notwithstanding this variation, input-

output relations can be readily specified. This

enables the development and implementation

of programmed controls such as procedure

guides and operating manuals e3c an t e and/or

the use of superior authority to ensure that

behavior is in accordance with that known to

achieve desired results. Accounting controls, on

the other hand, may be less effective when task

variety is high. Task variety inhibits the devel-

opment of pre-specified financial targets as it is

difficult to anticipate in advance the nature of

the tasks to be performed and thus to prepare

cost estimates which can serve as benchmarks

for measuring desired behavior. Further, beha-

vior controls will, as noted earlier, be more

timely than accounting controls. The complex

code of rules and procedures used to control

the behavior of airline pilots is a clear case of

where timeliness dictates their use in pref-

erence to

expost

controls.

However, it becomes difficult to use behavior

controls in Cell 4 if task variety (number of

exceptions) is very high. Behavior controls will

either need to be written at too general a level

to be useful, or the number of procedures

required to handle all the exceptions would be

prohibitively costly to document. Having ruled

out a role for accounting controls here, the

organization may need to resort to personnel

controls to achieve the coordination needed

for task execution. Indeed, it might be argued


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MANAGEMENT CONTROL SYSTEMS IN R D ORGANIZATIONS

237

that all “programmed” forms of control, such

as both accounting and behavior controls,

might be restricted to settings where the range

of tasks encountered is quite small (few excep-

tions). It is predicted that Cell 4 tasks will be

best controlled with either behavior or person-

nel controls, the latter perhaps constituting the

preferred choice.

In summary:

a>

@>

H3: Where tasks are low in analyzability

but few exceptions are encountered,

reliance on accounting controls will be

positively related to R&D management

performance.

H4: Where tasks are highly analyzable,

but many exceptions are encountered,

reliance on behavior and personnel con-

trols will be positively related to R&D

management performance.

THE EMPIRICAL STUDY

Sample

Survey data were collected by both ques-

tionnaire and interview, administered to 150

senior research officers in the research and

development (R&D) divisions of a large Austra-

lian industrial company and a major US scien-

tific organization.

Initial contact with both organizations was

through their respective Research Directors,

with whom the purpose of the study was dis-

cussed and agreement to participate secured.

Each Director gave approval to the study via the

provision of a covering memorandum on cor-

porate letterhead attached to the front of the

questionnaires, which were distributed via each

organization’s internal mail system. Stamped

envelopes for direct return of completed

questionnaires to the researchers accompanied

the questionnaires. Anonymity was assured,

both to the respective organizations and to all

respondents.

A total of 138 questionnaires (92 ) was

returned, eleven of which were incomplete,

resulting in a useable sample set of 127. With

only twelve of the distributed questionnaires

not being returned, and in view of the fact that

the assurances of anonymity prevented target-

ing any follow-up, no attempt was made to

supplement the sample size. On average, the

respondents were

40

years of age, had worked

for their present employer for 10 years, and had

been in their current position for three years.

The respondents all held first-line supervisory

positions and were responsible for managing

separate,

identifiable work groups. These

groups constituted the unit of analysis in the

study.

The question of whether the focus on R&D

organizations might cause range restriction in

the measures of the task variables was an issue

of concern. Indeed, Perrow (1970) offered

research organizations as an example of a Cell 3

organization (refer Fig. 1). The concern was

whether range restriction would place few

respondents in the other cells of Fig. 1. This

issue also arose in the study carried out by

Rockness and Shields (1988). Rockness and

Shields characterized the task setting of R&D in

terms of Ouchi’s (1979) framework which

focussed on output measurability and knowl-

edge of transformation process (Ouchi, 1979)

Rockness and Shields regarded R&D as uni-

formly low on both of Ouchi’s dimensions.

However, in the context of the present paper

we do not expect Cell 3 to be overpopulated

relative to other cells. We believe that there is

likely to exist considerable between work-

group variance in the two task dimensions as a

function of the type of R&D work undertaken.

In spite of the position taken by in the Rock-

ness and Shields, 1988 paper, they argued in

their earlier (Rockness & Shields, 1984) paper

that “basic research”, “applied research” and

“development” (National Science Foundation,

1976) were types of R&D activity that would

locate on a continuum in terms of task charac-

teristics with basic research at the more

“uncertain” end, and development at the more

“routine” end.

This issue was explored in preliminary inter-

views with senior management of both compa-

nies. The interview data confirmed that, in both


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238


companies,

research activities ranged from

basic, or fundamental research, often funded by

recurrent grants from corporate headquarters,

to applied and developmental activities nego-

tiated in arms length transactions, both with

other operating divisions within their respec-

tive companies, and with unrelated external

customers. In the words of one manager:

it (external work) is more routine in the sense that

there is a well defined contract set of requirements

that centres around routine schedules.. .the end goal

is more clearly specified than in corporate sponsored

research

On the basis of this evidence, it was felt that

cross-sectional variation in the task variables

would be sufficient to ensure that all four cells

of Fig. 1 were adequately populated.

Qu est i onn a i r e measu r es

S i x

variables were measured by the ques-

tionnaire: managerial performance, the two

task characteristics (number of exceptions and

task analyzability), accounting control, behavior

control and personnel control. Each is dis-

cussed in turn.

Per f o rmance .

The unit of analysis in the

study, as noted earlier, is the work group and

its manager, and it is this individual’s perfor-

mance for which measurement was sought. We

assessed overall performance via a self-rating,

single-item instrument, and used the taxonomy

developed by Mahoney

et a l .

(1963,1965) to

assess the proportion of the variance in the

overall measure accounted for by the eight sub-

dimensions of managerial performance. The

purpose of this test was not to validate the

overall rating as such, but rather to establish

whether or not variations in the overall rating

are accounted for by a set of sub-dimensions

developed originally by Mahoney

et a l .

(1963,

1965) to represent dimensions of managerial

work. While the Mahoney

et a l .

instrument has

been in the literature for a number of years, a

recent assessment of its validity suggests that

the functions identified by Mahoney

et a l .

“still

represent the most useful way of conceptualiz-

ing the manager’s job” and that the dimensions

are relevant across a variety of settings (Carroll

& Gillen, 1987).

The test was based on a multiple regression

of the overall rating on the eight sub-dimen-

sions, which produced a squared multiple cor-

relation coefficient of 0.52. Of interest is that

this coefficient is typical of those reported by

Mahoney

et aZ.

suggesting that what con-

stitutes “good performance” for R&D managers

is similar to that in other organizational func-

tions. In the hypothesis tests which follow, the

single-item overall rating of performance is used

as the criterion variable and this is consistent

with much prior research focusing on both

manufacturing and service industries at differ-

ing managerial levels (Abemethy Sr Stoelwin-

der, 1995; Brownell, 1982; Brownell & Dunk,

1991; Kren, 1992; McInnes & Ramakrishnan,

1991; Venkatraman & Ramanujam, 1987).

Ta sk cha r ac t e r i st i c s.

We used the Withey

et a t .

(1983) IO-item instrument, in seven-point

partially anchored form, to measure both task

analyzability and number of exceptions. Ortho-

gonal factor analysis of the 10 items (provided

in Appendix A) resulted in two factors con-

sistent with the two theoretical constructs of

interest, factor I capturing number of excep-

tions and factor II task analyzability. In the

analysis we employ summed scores for the set

of items loading on each factor, as opposed to

the factor scores because of the conventional

caution of the instability of factor loading solu-

tions. While this approach resulted in correla-

ted dimensions (r=O.59), this posed no serious

concern to our theoretical arguments. Indeed,

the framework constructed by Perrow (1979)

posited that the two dimensions would covary,

and prior empirical research examining the

relationship between the two constructs and

control system design also have found signifi-

cant and positive correlations of the same order

or magnitude to that reported here (Abemethy

& Stoelwinder, 1991; Van de ven & Delbecq,

1974; Daft & Macintosh, 1981; Withey

et a l .

1983). A test for the reliability of each dimen-

sion resulted in a Cronbach (1951) alpha coeffi-

cient of 0.91 for number of exceptions and 0.85

for task analyzability.


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239

Accounting controls.

The extent to which

accounting is used as a form of control was

captured in this study using a purpose-devel-

oped measure based on early work of Hop-

wood (1972) and others. Respondents were

asked to indicate on a seven-point partially

anchored Likert-type scale, the extent to which

each of three items represented the use of

accounting control in their unit. The items

included in the instrument are provided in

Appendix B. The highest inter-item correlation

obtained from the use of this measure was only

0.20, suggesting reliability problems and the need

to exercise caution in interpreting the results

which follow. These results were compared

with three further sets of results obtained using

each of the three items individually. No material

differences were found between the reported

results, which use the aggregate measure, and

any of the results using the individual measures.

Behavior controls.

Hage and Aiken’s 21-

item scale w s used in modified form to mea-

sure behavior control @Iage & Aiken, 1967).

The instrument was developed by Hage and

Aiken to measure the degree of centralization

and was initially conceived as having five

dimensions: participation in decision making,

hierarchy of authority, job codification, rule

observation and formalization. Behavior con-

trols have often been described in terms of the

last four dimensions of this instrument (Ouchi,

1979; Merchant, 1985) so the items relating to

these four dimensions were summed to mea-

sure the extent to which reliance is placed on

behavior control. The Cronbach alpha of 0.82

provides support for the reliability of measure.

While the Hage and Aiken instrument has been

in the literature for many years, its recent use

by other researchers (e.g., Banker

et al.,

1993),

who have found excellent reliability coeffi-

cients, supports its continuing suitability for

operationalizing this construct.

Personnel controls.

Recall that personnel

controls are the means to facilitate the opera-

tion of self- and group control processes. In

contrast to behavior and accounting controls,

personnel controls operate without imposing

formal bureaucratic forms of control. Instead,

they influence the types of individuals who

interact in the work group, the kind of profes-

sional activity undertaken by group members,

and the level of peer-group self-regulation, via

strict personnel selection, and perhaps place-

ment policies. By getting the “right” people,

the organization can rely on the professionalism

of group members and the sharing of common

values. The operation of self- and group con-

trols has been described by others as “clan”

control (Ouchi, 1980; Govindarajan & Fisher,

1990)

as

“social control” (e.g., Merchant,

1985; Rockness & Shields, 1988) and as “pro-

fessional control” (Orlikowsky, 1991; Aber-

nethy & Stoelwinder, 1995).

While we are interested in capturing the

extent to which tasks are controlled through

self- and group control processes, because of

the subtlety of these processes, it is difficult to

measure these directly. Indeed Birnberg (1988)

in his commentary on Rockness and Shields

(1988) cautions against using conventional

questionnaire measures to capture reliance on

these forms of control. Birnberg suggests that,

unlike accounting and behavior controls, pcr-

sonnel or group controls, because of their less

obvious manifestations, may be measurable

with conventional survey instruments only with

considerable error.

Hage and Aiken (1967) provide support for the

notion that an individual’s level of professional-

ism can be represented by the length of profes-

sional training and socialization processes to

which they are exposed. It is these processes

which lead professionals to behave according

to the collegial model of control noted by Per-

row (1970). Numerous other researchers also

support the idea that the operation of these

processes is directly related to external training

and socialization processes (Freidson, 1983;

Orlikowsky,

1991; Mintzberg, 1979). We,

therefore, use Hage and Aiken’s measure of

formal training (Hage & Aiken, 1967) to capture

personnel control as it would appear to be a

particularly appropriate proxy for representing

the presence of self- and group controls in R&D

settings which are often dominated by profes-

sionals. It was felt that the level of formal training


8/16


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241

the four cells. The purpose of this set of tests is

to ascertain the task situations to which each of

the three control types is best suited. The fol-

lowing equation was fitted for each control

type (suppressing the i subscript):

Y = bo + biXi + bzXa + b3Xb + b4Xc

+ b5XlX, + bGXlXb + b7X1Xc + e

(2)

where Y=performance

Xi=reliance on control type

types 2,3)

X,=1 for cell 2, 0 otherwise



1 (repeat for

The coefficients estimated by this regression

allow a statistical comparison of Xi’s effects

across the four cells of Fig. 1. To illustrate,

where X,=reliance on accounting controls, we

expect these to have more positive effects on

performance in Cells 1 and 2 than in either

Cells 3 or 4. Equation (2) reduces to:

Y b. + blX,

in Cell 1, so the coefficient b, captures the

effects of accounting controls on performance

in CelI 1. In Cell 2, equation (2) becomes:

Y =

bo +

bz) + (bl + bs)Xl

The coefficient b5 assesses the difference in

Table 1 also shows negative correlations

the effects of accounting controls in Cell 1 ver- between reliance on accounting control and

sus Cell 2. By parallel argument, bb assesses the

each of the task dimensions. This supports the

difference in the effects of accounting controls generalization, and the evidence from prior

in Cell 1 versus Cell 3 and b, allows comparison

with Cell 4. In terms of hypothesis 1, therefore,

we expect b5=0 because we hypothesize no

differential effects of accounting controls on

performance between Cells 1 and 2. However,

we expect

b6

and

b,

both to be negative

because we hypothesize less positive effects of

accounting controls on performance in both

Cells 3 and 4 compared to Cell 1. The expecta-

tions regarding behavior and personnel controls

were developed similarly and the upper panel

of Table 5 summarizes the specific expectations

for each control type.

RESULTS

Table 1 contains descriptive statistics as well

as the matrix of zero-order correlations among

all variables used in the study. For purposes of

all analysis, the items for both task dimensions

were scored so that high scores reflected high

task uncertainty. It is interesting to note the

virtual absence of any correlation among the

three control types. This suggests that, at the

organizational level, the reliance on any one

form of control is independent of any other. It

is well to recall, however, that this does not

rule out the prospect that at the level of par-

ticular sub-groupings of tasks (such as those

suggested in Fig. 1) considerable co-variation

might exist between particular forms of control

in place.

TABLE 1. Descriptive statistics and zero-order correlation matrix (significance) (n= 127)

VARIABLES MEAN S.D. Theoretical 1 2 3 4 5

Range

1. Managerial Performance 5.24 0.82 l-7

2. Task Analyzability 21.90 5.73 5-35 -0.12(0.09)

3. Number of Exceptions 26.82 5.49 5-35 0.10(0.14> 0.59(0.00)

4. Accounting Control

10.40 3.23 3-21 O.Ol(O.44) -0.26(0.00) -0.12(0.09)

5. Behavior Control

50.64 13.06 17-119 -0.09(0.17) -0.14(0.05) 0.15(0.04) 0.13(0.08)

6.

Personnel Control

5.30 0.96 1-6 0.33(0.00) 0.04(0.32) 0.07(0.23)

0.00(0.48) O.iO(O.13)


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4


work, that accounting controls tend to be de-

emphasized whenever task uncertainty is high.

Also noteworthy is the restricted range of

scores for personnel control; for all practical

purposes, the measure reduces to a binary vari-

able, respondents scoring either a 5 or a 6 on

the scale. This is a result which can perhaps, be

attributed to the choice of sample, and

although it does not affect interpretation of the

test results which follow, it raises the

need for

care when generalizing those results to other

occupational groups which may show less

extreme scores on the measure. Also likely to

be the result of sampling is the highly sig-

nificant and positive zero-order correlation

(0.33) between reliance on personnel forms of

control and performance.

To examine the hypotheses of the study, we

present first the results of fitting equation (1) to

the data in each cell of Fig. 1, along with selec-

tions of the interview data. Following this, the

results of fitting equation (2) are presented. The

results of fitting equation (1) to the data in Cells

1 through 4 of Fig. 1 are presented in Table 2.

Tables 2 and 4 report standardized beta coeffi-

cients so as to remove the effects of differing

scaling

used to measure the independent vari-

ables. The absolute magnitudes of the resulting

coefficients are, therefore, meaningfully com-

parable. Important in testing the hypotheses

are the results obtained when the coefficients

for each type of control are compared in each

cell. This set of results is presented in Table 3.

The results for Cell 1 show that use of

accounting control is the only variable which

significantly affects performance. Table 2

reports 2-tailed probabilities, but it is worthy of

note that the effect of using accounting con-

trols is positive, as predicted. In Cell 1 of

Table

3, the results show that the coefficient for

accounting controls (a,) is significantly larger

than the coefficient for behavior controls (aa),

but only at the 10 level of confidence, using a

l-tailed test. The coefficient for accounting

control was not significantly larger than the

coefficient for personnel controls. Hypothesis 1

predicted the importance of accounting con-

trols here, but also predicted equivalent impor-

tance for behavior controls. This hypothesis

was, therefore, only partially confirmed.

Hypothesis 2 stated that in Cell 3, personnel

controls would assume greatest importance.

The coefficient (a,) is significant and positive

(Table 2) and, importantly, is significantly

greater than the coefficients for both behavior

control (aa) and accounting control (at). It is

also interesting to note that accounting controls

have a significantly more positive effect on per-

formance than behavior controls in this setting.

Table

3

sets out these latter results which show

a clear preference ordering in Cell 3, the high-

est task uncertainty cell, with personnel

TABLE 2. Standardized beta coefficients and &statistics (significance levels’) from regressing managerial performance on

accounting, behavior and personnel controls. Equation (1)

Coefficient Cell 1

Cell 2 Cell 3 Cell 4

_

a,, constant 3.97 4.02 3.50 2.80

5.60 (0.00) 3.80 (0.00)

3.29 (0.00) 2.26 (0.04)

a, accounting control 0.30 - 0.10

0.06 - 0.27

1.98 (0.05) ~ 0.42 (0.68)

0.39 (0.70) - 1.41 (0.18)

az behavior control 0.03 - 0.03

- 0.21 0.21

0.19 (0.85) - 0.12 (0.90)

- 1.39 (0.17) 1.05 (0.31)

a3 personnel control 0.12 0.33

0.42 0.50

0.77 (0.44) 1.45 (0.16)

2.89 (0.01) 2.56 (0.02)

RZ 0.11 0.11

0.25 0.41

F 1.59 0.72

4.05 3.75

P

0.21 0.55

0.01 0.03

n 44 22

41 20

*

2-tailed tests.


11/16

‘MANAGEMENT CONTROL SYSTEMS IN R&D

ORGANIZATIONS

243

Low

Number of

Exceptions

High

TABLE 3. Summary of expectations and results [t-statistics

(significance)‘] of comparing the coefficients derived from

fitting equation (1).

Task analyzability

High Low

Expectations Expectations

Ll,=ai

al>aZ

aI’ a,‘

>a,

Results Results

a,>a2 t =1.30 (pCO.10) a,al t=1.76 @a, t=1.67 (pcO.05)

as>a, k2.53 @a2 t=2.64 (pa* t=1.41 @


12/16

244


Finally, consistent with the statistical results

which showed that behavior controls are the

least effective in Cell 3 type task situations, one

respondent offered this remark:

SO

ow the “control guys” over there are putting him

[the researcher] in a vice. He’s supposed to be doing

research, supposed to be developing ideas and

projects and steering people technology-wise. Now,

all of a sudden, he’s got this [a range of bureaucratic,

behavior controls] which puts him under constraints.

In all, the results provide strong support for

hypothesis 2.

Hypothesis 3 predicted a key role only for

accounting controls in Cell 2. The results fail to

confirm this. The coefficient for accounting

controls (ai) is not significant (Table 2), nor is

the overall regression itself. Of interest is the

result that personnel control, while not sig-

nificant itself (Table 2) shows significantly

more positive effects on performance than

accounting controls, but only at the 10 level

of confidence (Table 3).

Hypothesis 4 suggested that behavior con-

trols and personnel controls would be the most

effective choices in Cell 4. Again, the coeffi-

cient for behavior controls is, itself, not sig-

nificant (a2 in Table 2), but the results in

Table 3 show that it is significantly larger than

that for accounting controls (a,), as predicted.

As in the case of hypothesis 2, personnel con-

trols again emerge as important, relatively, in

that its coefficient is significantly larger than

that for accounting controls (al). These results

confirm hypothesis 4.

Apparently, the “exceptions” dimension of

the task is important to the effective operation

of personnel controls. Where this dimension is

high, personnel controls have highly significant,

positive effects on performance, regardless of

the level of task analyzability. By contrast, pro-

grammed controls,

such as accounting and

behavior controls do not appear compatible

with task situations involving many exceptions.

The results of fitting equation (2) to the data

for each control type are presented in Table 4

and a summary of the relevant tests is contained

in Table 5. With regard to accounting controls,

the results show that their most positive effects

are in Cell 1 (bl>O). We predicted similar

effects in Cell 2 and while bs=O at conventional

probability levels, it is tending negative, sug-

gesting a less positive role in Cell 2. The less

positive effects of accounting controls in Cells

3

and 4 were predicted;

bb and b,, are

both

negative, although at conventional leveis only

b, is significant.

Behavior controls were predicted to have

most positive effects in Cells 1 and 4. Neither

result is confirmed (bI=O and b,, while positive,

is not significant). Of note is the significantly

@=O.lO) less positive effects of behavior con-

trols in Cell 3. Moreover, a comparison of the

coefficients bb and b, reveals that they are sig-

nificantly different (t= 1.63,

pO

b,>O

bG>O

b5=0

b,=O

b,=O

b6, b,0

[p=O.O4]

b5=0

bT=O

b,=O

b =0 b5=0

b5=b6=by

b,


13/16


245

2. Reliance on accounting controls has sig-

nificant positive effects on performance

only where task uncertainty is lowest

(Cell 1). The prediction that it would have

similar effects in Cell 2 was not borne out.

3. The task dimension, number of excep-

tions, appears to have the greater bearing

on the suitability of personnel controls

and the unsuitability of either accounting

or behavior controls.

4. Behavior controls appear to contribute

positively to performance in no situation,

and contribute negatively where task

uncertainty is highest (Cell 3) although

they are preferred to accounting controls

in Cell 4. However, it is the importance of

personnel controls in Cells 3 and 4 and

the need to avoid reliance on either

accounting or behavior controls in such

task situations, which constitutes the main

result of the paper.

DISCUSSION, LIMITATIONS AND

CONCLUSIONS

The management accounting literature has

paid some attention to the question of the design

of control systems in research and development

organizations and this paper extends that line

of inquiry. The tasks undertaken in such orga-

nizations are likely to include some which are

relatively extreme in terms of task uncertainty.

Using Perrow’s model of technology and struc-

ture (Pet-row, 1970) this study sought to

examine the effectiveness of conventional

accounting controls, behavior controls, such as

procedure guides and job codification, and per-

sonnel controls in settings which varied in

terms of task analyzability and the number of

exceptions encountered in task execution.

The main contribution of the study is the

finding which shows that where task uncer-

tainty is highest (that is, where task analyz-

ability is low and number of exceptions high)

reliance on personnel forms of control has a

positive and significant effect on performance.

Moreover, this effect is significantly more posi-

tive than that of either accounting or behavior

controls. This confirms the theoretical expecta-

tions of the study.

Also consistent with expectations was the

result that behavior controls prove significantly

more effective than accounting controls in Cell

4 of Fig. 1. Tasks high in analyzability, but also

high in number of exceptions, are not as amen-

able to accounting forms of control. This is

because such tasks, while analyzable when they

arise, cannot be foreshadowed with sufficient

notice to enable the ex ante specification of

input-output relations necessary for the use of

many forms of accounting control (e.g., stan-

dard costing).

The results of the study should be considered

in light of its limitations. First, it is necessary

to acknowledge the usual limitation of cross-

sectional based survey research, namely that

such a study design does not permit causal

statements. Inferences of causation implied are

all based on theoretical positions taken. Second,

variable measurement remains an area worthy

of further research attention. While a recog-

nized measure of task characteristics was used

in the study, the measurement of reliance on all

forms of control needs further development,

especially in the area of personnel controls,

where an unproven proxy was employed. We

rely on two separate tests of convergent valid-

ity, and on the strong, theoretically consistent

results for the role of personnel controls to

vindicate the means of its measurement, but

readily acknowledge that the latter is an

expost

rationalization and that there exists much scope

for further developmental work in this area.

The use of a self-rating performance measure

may constitute a limitation of the study. How-

ever, there are few alternative means available

to capture managerial performance with a

cross-sectional sample. We were unable to use

superior ratings as we guaranteed anonymity to

the respondents and there were also no appar-

ent objective and comparative measures which

could be used reliably to capture performance

across our sample of respondents.

Third, the study stopped short of predicting/

estimating the interactive effects of various


14/16

246 M. A. ABERNETHY and P. BROWNELL

controls on performance. That is, we did not

consider the prospect that, for example, the

effectiveness of personnel controls in a given

task situation may differ according to the

degree of reliance placed simultaneously on

accounting controls. The data gathered are suf-

ficient to estimate such interactions, but the

significant prospect of experimenter-wise error

cautioned against this. When various measure-

ment issues are better settled, and when a more

solid theoretical foundation is developed for the

prediction of such effects, such a study should

be undertaken.

Notwithstanding these limitations, the results

of the study have provided clear evidence, in

support of prior research, of settings not suited

to the use of accounting forms of control. The

more important contribution of the study has

been to identify the potential of non-accounting

controls, especially personnel forms of control,

to contribute to the overall management con-

trol effort of the organization where task char-

acteristics are not well suited to the use of

traditional, accounting based controls.

Future research could usefully focus on sev-

eral related avenues of enquiry. First, it could

provide further evidence confirming the results

of the present study, while overcoming some of

its limitations. Second, the external validity of

the present study’s findings could be enhanced

by extending the inquiry to other organizational

functions and settings. And third, with a

strengthened theoretical basis, research could

begin to explore the implications of combina-

tions of controls, studying their interactive

effects on important organizational outcomes.

It is clear that organizations rely on combina-

tions of control mechanisms in any given set-

ting, yet virtually nothing is known about how

the effects of any one control are governed by

the level of simultaneous reliance on other

forms. Until empirical work begins to examine

this complex question, our understanding of

how the full range of management controls

operates will remain piecemeal. Finally, we are

inclined to suggest that the treatment of

accounting controls in our framework deserves

further thought. In particular, the view that

accounting information is limited to the exer-

cise of control based on just outputs ignores its

potential role (such as in the context of cost

centres) as a form of behavior control. Further

research would do well to distinguish these two

potential roles.

BIBLIOGRAPHY

Abernethy, M. A., & Stoelwinder, J. U. (1991). Budget use, task uncertainty, system goal orientation and

subunit performance: a test of the “fit” hypothesis in not-for-profit hospitals. Accounting, Organizations

andSociety,

16, 105-120.

Abernethy, M. A., & Stoelwinder, J. IJ. (1995). The role of professional control in the management of

complex organizations.

Accounti ng, Orguni zutions and Society, 20,

1- 18.

Banker, R. D., Potter, G., & Schroeder, R. G. (1993). Reporting manufacturing performance measures to

workers: an empirical study.

Journal of Management Accounting Research,

Fall,

33-55.

Bimberg, J. G. (1988). Discussion of an empirical analysis of the expenditure budget in reseach and

development.

Contemporar y Accounti ng Research, 582-587

Brownell, P. (1982). The role .of accounting data in performance evaluation, budgetary participation and

organizational effectiveness.

Journal of Accounting Research,

Spring, 12-27.

Brownell, P., 8r Dunk, A. S. (1991). Task uncertainty and its interaction with budgetary participation and

budget emphasis: some methodological issues and empirical investigation.

Accounting, Organizations

and Society, 16, 693-703.

Brownell, P., & Hirst, M. (1986). Reliance on accounting information, budgetary participation and task

uncertainty: tests of a three-way interaction. Journal of Accounting Research, Autumn, 241-249.

Bruns, W. J., & Waterhouse, J. H. (1975). Budgetary control and organization structure, Journal of

Accounting Research, Autumn, 177-203.

Carroll, S. J., & Gillen, D. J. (1987). Are the classical management functions useful in describing managerial

work?

Academy of Management Review, 1, 38-51.


15/16


247

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psycbometrika, 297-334.

Daft, R. L., & Macintosh, N. 3. (1981). A tentative exploration into the amount and equivocal&y of infor-

mation processing in organizational work units.

Admini strative Science Quar terfy,

June, 207-224.

Foster, G., & Gupta, M. (1994). Marketing, cost management and management accounting. Journal of

Management Accounti ng Research,

Fall,

43-77.

Freidson, E. (1983). The theory of professions: state of the art. In R. Kingwall & P. Lewis (Eds) The

Socfology of

Profession.

London: Macmillan Press.

Govindarajan, V., & Fisher, J. (1990). Strategy, control systems and resource sharing: effects on business

unit performance. Academy of Management Journal. 259-285.

Hage, J., & A&en, M. (1967). Relationship of centralization to other structural properties. Administrative

Science QuarterIy

June, 72-92.

Hirst. M. K. (1983). Reliance on accounting performance measures, task uncertainty and dysfunctional

behavior: some extensions.


Autumn, 596-605.

Hopwood, A. G. (1972). An empirical study of the role of accounting data ln performance evaluation.


Supplement, 156-182.

Kren, L. (1992). Budgetary participation and managerial performance: the impact of information and

environmental volatility. The

Accounting Review,

July, 51 l-526.

Mclnnes, M.. & Ramakrishnan, R. ‘T. S. (1991). A decision theory model of motivation and its usefulness in

the diagnosis of management control systems.

Accounting, Organizations

and Society, 16 167- 184.

Mahoney, T. A., Jerdee, T. H., & Carroll, S. J. (1963). Deveiopment of manageri al perf ormance: a

research approach. Cincinnati: South-Western Publishing Co.

Mahoney, T. A., Jerdee, T. H., 8t Carroll, S. J. (1965). The jobs of management.

I ndustri al Relations,

Feb-

ruary, 97-l 10.

Merchant, K. A. (1985).

Contr ol i n business or ganizations.

Boston: Pitman.

Mintzberg, H. (1979). The

structuri ng of organizations.

Englewood Cliffs: Prentice Hall.

National Science Foundation (1976). Nationalpatterns of R D resources: funds and manpower in the

United States, 1953-197G. NSF.

Orlikowsky, W. J. (1991). Integrated information environment or matrix of control? The contradictory

implications of information technology.

Accounting, Management and I nformation Technologies, 9-

42.

Ouchi, W. G. (1979). A conceptual framework for the design of organizational control mechanisms.

Management Science, September, 833-848.

Ouchi, W. G. (1980). Markets, bureaucracies and clans.

Admini strative Science Quar terly,

March, l29-

141.

Perrow, C. (1970).

Organ izationat analysis: a sociol ogfcal view. New

York: Tavistock Publications.

Perrow, C. (1979) Organ izational analysis: a sociol ogical view. London: Tavistock Publications.

Rockness. H. O., & Shields, M. D. (1984). Organizational control systems in research and development,

Accounti ng, Organi zations and Society, 9, 165-177.

Rockness. H. O., & Shields, M. D. (1988). An empirical analysis of the expenditure budget in research and

development.

Contemporary Accounti ng Research, 568-581.

Van de ven, A. H., & Delbecq, A. L. (1974). A task contingent model of work-unit structure.

Adminis-

trative Science Quarterly, June, 183-197.

Venkatraman, N., & Ramanujam, V. (1987). Measurement of business economic performance: an exami-

nation of method convergence.

Journal of

Management, Spring, 109-l 22

Withey, M., Daft R. L.. & Cooper, W. H. (1983). Measures of Perrow’s work unit technology: an empirical

assessment of a new scale.

Academy ofM anugementJour nal,

45-63.


16/16

248


APPENDIX A. FACTOR ANALYSIS OF THE lo-ITEM TASK UNCERTAINTY MEASURE

ITEMS

How repetitious are the duties of those in your unit?

To what extent would you say the work of your unit is routine?

Basically, unit members perform repetitive activities in doing their jobs.

How many of the tasks in your unit are the same from day to day?

People in my unit do about the same job in the same way most of the time.

To what extent is there an understandable sequence of steps that can be

followed in doing the work of your unit?

To what extent is there an understandable sequence of steps that can be

followed in carrying out the work in your unit?

To what extent is there a clearly known way to do the major types of work

normally encountered in your unit?

To what extent is there a clearly defined body of knowledge of subject

matter which can guide the work done in your unit?

To do the work of your unit, to what extent can personnel actually rely on

established procedures and practices?

Variance explained

FACTOR

I

II

0.876

0.863

0.825

0.779

0.75 1

0.799

0.773

0.727

0.720

0.696

54.2

14.6

APPENDIX B. ITEMS USED TO MEASURE ACCOUNTING CONTROL AND

PERSONNEL CONTROL

(a) Accounting control measure

The items in this section have the purpose of assessing the role you feel that accounting infor-

mation plays in the evaluation of your own performance. That is, to what extent does your

superior rely on accounting information such as budgets, spending limits, and financial targets,

in judging your performance. For each item,

please ci rcle one number.

(l=Definitely false,

7=Definitely true).

1. No accounting numbers of any sort figure prominently when my performance is judged

(reverse scored).

2. Control over the activities of research departments is achieved principally with periodic

reporting of accounting information.

3. My ability to meet financial targets is a preoccupation of my superior.

(b) Proxy measure used for personnel control

(Hage & Aiken, 1967)

Respondents were asked to check the appropriate response category. Responses were coded

1-6 with lowest score being l(a) and highest score being 6(f). High scores proxy for extensive

reliance on personnel control.

What level of formal education did you complete? (Check One)

a. High school graduate (or less) with no professional training

b. High school graduate (or less) with some professional training

c. College degree, or some college education, but no professional training

d. College degree, or some college education and some professional training

e. Post-graduate degree, but no professional training

f. Post-graduate degree and some professional training

4 - abernethy & brownell 1997 - management control systems

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